Maximum likelihood autoregressive model parameter estimation with noise corrupted independent snapshots

Çayır, Ömer; Candan, Çağatay

doi:10.1016/j.sigpro.2021.108118

Cited by 7 publications

(3 citation statements)

References 32 publications

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“…used the maximum likelihood principle to reconstruct the parameters of Bernoulli autoregressive systems [ 22 ]. Çayır and Candan developed a parameter estimation approach for autoregressive models through using the maximum likelihood principle [ 23 ]. By using the maximum likelihood principle, this paper derived the maximum likelihood fitness and proposes two novel algorithms to estimate the moisture ratio model of a cantaloupe drying process.…”

Section: Introductionmentioning

confidence: 99%

Estimating the Moisture Ratio Model of Cantaloupe Slices by Maximum Likelihood Principle-Based Algorithms

Zhu

Raghavan

2023

Plants

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As an agricultural plant, the cantaloupe contains rich nutrition and high moisture content. In this paper, the estimation problem of the moisture ratio model during a cantaloupe microwave drying process was considered. First of all, an image processing-based cantaloupe drying system was designed and the expression of the moisture ratio with regard to the shrinkage was built. Secondly, a maximum likelihood principle-based iterative evolution (MLP-IE) algorithm was put forward to estimate the moisture ratio model. After that, aiming at enhancing the model fitting ability of the MLP-IE algorithm, a maximum likelihood principle-based improved iterative evolution (MLP-I-IE) algorithm was proposed by designing the improved mutation strategy, the improved scaling factor, and the improved crossover rate. Finally, the MLP-IE algorithm and MLP-I-IE algorithm were applied for estimating the moisture ratio model of cantaloupe slices. The results showed that both the MLP-IE algorithm and MLP-I-IE algorithm were effective and that the MLP-I-IE algorithm performed better than the MLP-IE algorithm in model estimation and validation.

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Section: Introductionmentioning

confidence: 99%

Estimating the Moisture Ratio Model of Cantaloupe Slices by Maximum Likelihood Principle-Based Algorithms

Zhu

Raghavan

2023

Plants

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“…One-step localization method [21][22][23][24][25][26][27][28][29][30][31][32][33][34][35] processes the observed signals in the central processor without parameter extraction and then constructs the cost function only related to the position of the emitter. This kind of localization algorithms finally obtain the position of the emitter through the maximum likelihood estimation (MLE), the least squares, the grid search, gradient-based methods, etc.…”

Section: Introductionmentioning

confidence: 99%

“…In 2004, Weiss first proposed direct position determination technology [21], analyzed, and compared the DPD performance with AOA for signals with known (DPD-known) and unknown waveforms (DPD-unknown) [22,23]. Over the past dozen years, multiple DPD algorithms have been presented to enhance performance of passive localization system based on different approaches including the maximum likelihood (ML) [25,26], the time frequency analysis (DPD-STFT-w) [27], the multiple signal classification (MUSIC) [28,29], the expectation maximization (EM-DPD) [30,31], the minimum variance distortionless response (MVDR) [32][33][34], and the time-varying delay [35]. Compared with the one-step approaches, the DPD algorithms directly estimate the initial position of the target according to the received signals in the central station, avoiding the process of correlation of parameters of different receivers.…”

Section: Introductionmentioning

confidence: 99%

A Fast Direct Position Determination Algorithm for LFM Signal Based on Spectrum Detection

2022

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For the purpose of simultaneously estimating and locating the linear frequency modulation (LFM) emitter with unknown parameters, an innovative approach, i.e., Fast Direct Position Determination (FDPD), is introduced. The proposed algorithm is based on maximum likelihood estimation (MLE) and spectrum detection. To improve the accuracy and overcome the dramatic complexity of plain maximum likelihood formulation, we further derive the objective function equation of Direct Position Determination (DPD) algorithm and present an enhanced strategy to solve the highly nonlinear optimization problem. By combining the two-step localization method, one-step localization method, and short-time Fourier transform (STFT), our approach realizes jointly estimation of the transmitted signal parameters and emitter localization. Simulation results show that the proposed method is superior compared to the existing DPD, and two-step localization algorithms in terms of localization error and computational complexity, especially for low signal-to-noise ratio (SNR).

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Identification and validation of periodic autoregressive model with additive noise: finite-variance case

Żuławiński

Grzesiek

Zimroz

et al. 2023

Journal of Computational and Applied Mathematics

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Maximum likelihood autoregressive model parameter estimation with noise corrupted independent snapshots

Cited by 7 publications

References 32 publications

Estimating the Moisture Ratio Model of Cantaloupe Slices by Maximum Likelihood Principle-Based Algorithms

Estimating the Moisture Ratio Model of Cantaloupe Slices by Maximum Likelihood Principle-Based Algorithms

A Fast Direct Position Determination Algorithm for LFM Signal Based on Spectrum Detection

Identification and validation of periodic autoregressive model with additive noise: finite-variance case

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